When AI Remembers: Investigating the Dual Role of Long‐Term Memory in Chatbots

ABSTRACT This research investigates the dual role of long‐term memory (LTM) in AI chatbots and its impact on customer outcomes. Drawing on Mind Perception Theory and Privacy Calculus Theory, we conceptualize LTM as a continuity‐based design capability that may be interpreted as a functional service feature, a privacy‐relevant cue, or a basis for anthropomorphic inference. Across three controlled scenario‐based online experiments in e‐commerce customer service, we manipulated the presence of LTM and, in Study 3, the provision of user data control within a memory‐enabled condition. Results show that LTM significantly increased satisfaction and continuance intention, while its effect on word‐of‐mouth (WoM) intention was primarily indirect. Specifically, LTM improved outcomes through enhanced competence, although these benefits were partially offset by perceived anthropomorphism and privacy concerns. In Study 2, baseline perception of privacy empowerment was insufficient to mitigate privacy concerns associated with LTM; however, within a memory‐enabled condition, introducing an explicit user data‐control mechanism in Study 3 increased perceived privacy empowerment, which in turn reduced privacy concerns and improved outcomes via serial mediation. These findings show how explicit user data control can help manage privacy concerns within memory‐enabled chatbot representations and provide guidance for designing memory‐enabled chatbots with robust user‐control mechanisms.